{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "gold_data=pd.read_csv('LBMA-GOLD.csv')\n",
    "bit_data=pd.read_csv('BCHAIN-MKPRU.csv')\n",
    "a_g=np.array(np.array(range(1,len(gold_data['Date'])+1)))\n",
    "b_g=np.array(gold_data['Date'])\n",
    "a_b=np.array(np.array(range(1,len(bit_data['Date'])+1)))\n",
    "b_b=np.array(bit_data['Date'])\n",
    "table_g=np.vstack([a_g,b_g]).T\n",
    "table_b=np.vstack([a_b,b_b]).T\n",
    "\n",
    "def dayb2dayg(day_b):\n",
    "    try:\n",
    "        for li in table_b:\n",
    "            if li[0]==day_b:\n",
    "                date=li[1]\n",
    "        for li in table_g:\n",
    "            if li[1]==date:\n",
    "                day_g=li[0]\n",
    "        return day_g,date\n",
    "    except:\n",
    "        return (False,date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bit_data=np.array(bit_data)\n",
    "gold_data=np.array(gold_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_g=pd.read_csv('prediction_g.csv',header=None)\n",
    "pre_b=pd.read_csv('prediction_b.csv',header=None)\n",
    "pre_g=np.array(pre_g)\n",
    "pre_g[:,0].astype(int)\n",
    "pre_b=np.array(pre_b)\n",
    "pre_b[:,0].astype(int)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "609.96"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_b[5-5][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设天数为day_b取该日两产品价格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# today is day_b (day_b>=5)\n",
    "def day_info(day_b):\n",
    "    if day_b<5:\n",
    "        raise ValueError('day_b must bigger than 5')\n",
    "    if dayb2dayg(day_b)[0]!=False:\n",
    "        (day_g,date)=dayb2dayg(day_b)\n",
    "        value_b=bit_data[day_b-1][1]\n",
    "        value_b_p=pre_b[day_b-5][1]\n",
    "        value_b_pp=pre_b[day_b-5][2]\n",
    "        value_g=gold_data[day_g-1][1]\n",
    "        value_g_p=pre_g[day_g-5][1]\n",
    "        value_g_pp=pre_g[day_g-5][2]\n",
    "        info=(True,value_g,value_g_p,value_g_pp,value_b,value_b_p,value_b_pp,date)\n",
    "        return info\n",
    "    else: # gold cannot be treated this day\n",
    "        date=dayb2dayg(day_b)[1]\n",
    "        value_b=bit_data[day_b-1][1]\n",
    "        value_b_p=pre_b[day_b-5][1]\n",
    "        value_b_pp=pre_b[day_b-5][2]\n",
    "        info=(False,value_b,value_b_p,value_b_pp,date)\n",
    "        return info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, 1794.6, 1817.6, 1836.1, 46368.69, 47281.0, 48644.0, '9/10/21')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_info(1826)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def calculate(alpha_g):\n",
    "    # alpha_g=0.01\n",
    "    alpha_b=2*alpha_g\n",
    "    n_c=1000\n",
    "    n_g=0\n",
    "    n_b=0\n",
    "    R=1.4\n",
    "    R2=0.014\n",
    "    list_c=[]\n",
    "    list_g=[]\n",
    "    list_b=[]\n",
    "    list_v=[]\n",
    "    for day_b in range(5,1827):\n",
    "        info=day_info(day_b)\n",
    "        if info[0]==True: # gold can be treated\n",
    "            g_increase1=(info[2]-info[1])/info[1]\n",
    "            g_increase2=(info[3]-info[1])/info[1]\n",
    "            b_increase1=(info[5]-info[4])/info[4]\n",
    "            b_increase2=(info[6]-info[4])/info[4]\n",
    "            if g_increase1<-0.01 and b_increase1<-0.02:\n",
    "                n_g_i=n_g*(1+g_increase1*R)\n",
    "                n_b_i=n_b*(1+b_increase1*R)\n",
    "                n_c_i=n_c+n_g*abs(g_increase1)*R/(1+alpha_g)+n_b*abs(b_increase1)*R/(1+alpha_b)\n",
    "            if g_increase1<-0.01 and b_increase1>0.02:\n",
    "                n_g_i=n_g*(1+g_increase1*R)\n",
    "                n_b_i=n_b+(n_g*abs(g_increase1)*R)*info[1]/(info[4]*(1+alpha_b)*(1+alpha_g))\n",
    "                n_c_i=n_c\n",
    "            if g_increase1>0.01 and b_increase1<-0.02:\n",
    "                n_b_i=n_b*(1+b_increase1*R)\n",
    "                n_g_i=n_g+(n_b*abs(b_increase1)*R)*info[4]/(info[1]*(1-alpha_b)*(1+alpha_g))\n",
    "                n_c_i=n_c\n",
    "            if g_increase1>0.01 and b_increase1>0.02:\n",
    "                n_g_i=n_g+n_c*R2*(g_increase1/(g_increase1+b_increase1))/(info[1]*(1+alpha_g))\n",
    "                n_b_i=n_b+n_c*R2*(b_increase1/(g_increase1+b_increase1))/(info[4]*(1+alpha_b))\n",
    "                n_c_i=n_c*(1-R2)\n",
    "            if 0.01>g_increase1>-0.01 and b_increase1>0.02:\n",
    "                n_b_i=n_b+n_c*R2/(info[4]*(1+alpha_b))\n",
    "                n_c_i=n_c-n_c*R2\n",
    "                n_g_i=n_g\n",
    "            if 0.01>g_increase1>-0.01 and b_increase1<-0.02:\n",
    "                n_b_i=n_b*(1+b_increase1*R)\n",
    "                n_c_i=n_c+n_b*abs(b_increase1)*R*info[4]/(1+alpha_b)\n",
    "                n_g_i=n_g\n",
    "            if 0.02>b_increase1>-0.02 and g_increase1<-0.01:\n",
    "                n_g_i=n_g*(1+g_increase1*R)\n",
    "                n_c_i=n_c+n_g*abs(g_increase1)*R*info[1]/(1+alpha_g)\n",
    "                n_b_i=n_b\n",
    "            if 0.02>b_increase1>-0.02 and g_increase1>0.01:\n",
    "                n_g_i=n_g+n_c*R2/(info[1]*(1+alpha_g))\n",
    "                n_c_i=n_c-n_c*R2\n",
    "                n_b_i=n_b\n",
    "            if 0.02>b_increase1>-0.02 and 0.01>g_increase1>-0.01:\n",
    "                n_c_i=n_c\n",
    "                n_g_i=n_g\n",
    "                n_b_i=n_b\n",
    "            n_g=n_g_i\n",
    "            n_b=n_b_i\n",
    "            n_c=n_c_i\n",
    "            temp=info[1]\n",
    "            list_v.append(n_c+info[1]*n_g+info[4]*n_b)\n",
    "            # print(g_increase1,b_increase1)\n",
    "        elif info[0]==False:\n",
    "            b_increase1=(info[2]-info[1])/info[1]\n",
    "            b_increase2=(info[3]-info[1])/info[1]\n",
    "            if b_increase1>0.01:\n",
    "                n_b_i=n_b+n_c*R2/(info[1]*(1+alpha_b))\n",
    "                n_c_i=n_c*(1-R2)\n",
    "                n_g_i=n_g\n",
    "            if b_increase1<-0.01:\n",
    "                n_b_i=n_b*(1+b_increase1*R)\n",
    "                n_c_i=n_c+n_b*abs(b_increase1)*R*info[1]/(1+alpha_b)\n",
    "                n_g_i=n_g\n",
    "            n_g=n_g_i\n",
    "            n_b=n_b_i\n",
    "            n_c=n_c_i\n",
    "            list_v.append(n_c+temp*n_g+n_b*info[1])\n",
    "        list_c.append(n_c_i)\n",
    "        list_g.append(n_g_i)\n",
    "        list_b.append(n_b_i)\n",
    "        # print(n_c_i,n_g_i,n_b_i)\n",
    "    # print(n_c_i+n_g_i*1794.6+n_b_i*46368.69)\n",
    "    return n_c_i+n_g_i*1794.6+n_b_i*46368.69"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# info=day_info(1826)\n",
    "# sum=n_c+n_g*info[1]+n_b*info[4]\n",
    "# sum\n",
    "# print(n_c_i+n_g_i*1794.6+n_b_i*46368.69)\n",
    "# print(n_c_i,n_g_i,n_b_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "# plt.figure(figsize=(12,6))\n",
    "# plt.plot(list_v)\n",
    "# plt.xlabel('Days')\n",
    "# plt.ylabel('USD')\n",
    "# plt.title('Total Assets by Days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig=plt.figure(figsize=(18,6))\n",
    "# ax1=fig.add_subplot(131)\n",
    "# ax1.plot(list_c)\n",
    "# ax1.set_title('Cash by Days')\n",
    "# ax1.set_xlabel('Days')\n",
    "# ax1.set_ylabel('USD')\n",
    "# ax2=fig.add_subplot(132)\n",
    "# ax2.plot(list_g)\n",
    "# ax2.set_title('Sum of Gold by Days')\n",
    "# ax2.set_xlabel('Days')\n",
    "# ax2.set_ylabel('Ounce')\n",
    "# ax3=fig.add_subplot(133)\n",
    "# ax3.plot(list_b)\n",
    "# ax3.set_title('Sum of Bit Coin by Days')\n",
    "# ax3.set_xlabel('Days')\n",
    "# # ax3.set_ylabel('USD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24899.6700149263"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate(0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "l=[]\n",
    "for i in range(1,100):\n",
    "    alpha_g=i/1000\n",
    "    total=calculate(alpha_g)\n",
    "    l.append([alpha_g,total])\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.001, 25745.894652455696],\n",
       " [0.002, 25649.099108513565],\n",
       " [0.003, 25553.01288972495],\n",
       " [0.004, 25457.628707176522],\n",
       " [0.005, 25362.939367243725],\n",
       " [0.006, 25268.937770102668],\n",
       " [0.007, 25175.61690826945],\n",
       " [0.008, 25082.96986516504],\n",
       " [0.009, 24990.989813707114],\n",
       " [0.01, 24899.6700149263],\n",
       " [0.011, 24809.003816607605],\n",
       " [0.012, 24718.98465195643],\n",
       " [0.013, 24629.6060382883],\n",
       " [0.014, 24540.86157574172],\n",
       " [0.015, 24452.74494601394],\n",
       " [0.016, 24365.24991111936],\n",
       " [0.017, 24278.370312170176],\n",
       " [0.018, 24192.10006817758],\n",
       " [0.019, 24106.433174875234],\n",
       " [0.02, 24021.363703562285],\n",
       " [0.021, 23936.885799968462],\n",
       " [0.022, 23852.993683136232],\n",
       " [0.023, 23769.681644325097],\n",
       " [0.024, 23686.944045933775],\n",
       " [0.025, 23604.77532044115],\n",
       " [0.026, 23523.169969365434],\n",
       " [0.027, 23442.122562242268],\n",
       " [0.028, 23361.627735618837],\n",
       " [0.029, 23281.68019206672],\n",
       " [0.03, 23202.27469921147],\n",
       " [0.031, 23123.406088777712],\n",
       " [0.032, 23045.06925565215],\n",
       " [0.033, 22967.259156961045],\n",
       " [0.034, 22889.970811164785],\n",
       " [0.035, 22813.199297167048],\n",
       " [0.036, 22736.939753438197],\n",
       " [0.037, 22661.18737715603],\n",
       " [0.038, 22585.937423358],\n",
       " [0.039, 22511.185204110334],\n",
       " [0.04, 22436.926087688644],\n",
       " [0.041, 22363.1554977744],\n",
       " [0.042, 22289.868912663813],\n",
       " [0.043, 22217.06186448971],\n",
       " [0.044, 22144.729938457058],\n",
       " [0.045, 22072.868772090256],\n",
       " [0.046, 22001.474054494065],\n",
       " [0.047, 21930.54152562554],\n",
       " [0.048, 21860.06697557902],\n",
       " [0.049, 21790.046243882167],\n",
       " [0.05, 21720.475218803665],\n",
       " [0.051, 21651.349836673005],\n",
       " [0.052, 21582.66608120986],\n",
       " [0.053, 21514.4199828659],\n",
       " [0.054, 21446.607618176913],\n",
       " [0.055, 21379.225109125015],\n",
       " [0.056, 21312.26862251114],\n",
       " [0.057, 21245.734369338763],\n",
       " [0.058, 21179.61860420601],\n",
       " [0.059, 21113.917624709135],\n",
       " [0.06, 21048.627770854167],\n",
       " [0.061, 20983.74542447904],\n",
       " [0.062, 20919.26700868421],\n",
       " [0.063, 20855.18898727336],\n",
       " [0.064, 20791.50786420156],\n",
       " [0.065, 20728.220183033707],\n",
       " [0.066, 20665.322526410528],\n",
       " [0.067, 20602.81151552363],\n",
       " [0.068, 20540.68380959841],\n",
       " [0.069, 20478.936105385415],\n",
       " [0.07, 20417.565136659974],\n",
       " [0.071, 20356.567673728627],\n",
       " [0.072, 20295.94052294468],\n",
       " [0.073, 20235.68052623056],\n",
       " [0.074, 20175.78456060746],\n",
       " [0.075, 20116.249537733],\n",
       " [0.076, 20057.072403445374],\n",
       " [0.077, 19998.250137315386],\n",
       " [0.078, 19939.779752204293],\n",
       " [0.079, 19881.658293829718],\n",
       " [0.08, 19823.882840337385],\n",
       " [0.081, 19766.450501879935],\n",
       " [0.082, 19709.35842020203],\n",
       " [0.083, 19652.603768231726],\n",
       " [0.084, 19596.183749678494],\n",
       " [0.085, 19540.095598636835],\n",
       " [0.086, 19484.33657919652],\n",
       " [0.087, 19428.903985058292],\n",
       " [0.088, 19373.795139155925],\n",
       " [0.089, 19319.00739328334],\n",
       " [0.09, 19264.538127728178],\n",
       " [0.091, 19210.384750910067],\n",
       " [0.092, 19156.5446990253],\n",
       " [0.093, 19103.015435695783],\n",
       " [0.094, 19049.794451624475],\n",
       " [0.095, 18996.87926425486],\n",
       " [0.096, 18944.26741743657],\n",
       " [0.097, 18891.956481095003],\n",
       " [0.098, 18839.944050906633],\n",
       " [0.099, 18788.2277479789]]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 0.04,\n",
       " 0.041,\n",
       " 0.042,\n",
       " 0.043,\n",
       " 0.044,\n",
       " 0.045,\n",
       " 0.046,\n",
       " 0.047,\n",
       " 0.048,\n",
       " 0.049,\n",
       " 0.05,\n",
       " 0.051,\n",
       " 0.052,\n",
       " 0.053,\n",
       " 0.054,\n",
       " 0.055,\n",
       " 0.056,\n",
       " 0.057,\n",
       " 0.058,\n",
       " 0.059,\n",
       " 0.06,\n",
       " 0.061,\n",
       " 0.062,\n",
       " 0.063,\n",
       " 0.064,\n",
       " 0.065,\n",
       " 0.066,\n",
       " 0.067,\n",
       " 0.068,\n",
       " 0.069,\n",
       " 0.07,\n",
       " 0.071,\n",
       " 0.072,\n",
       " 0.073,\n",
       " 0.074,\n",
       " 0.075,\n",
       " 0.076,\n",
       " 0.077,\n",
       " 0.078,\n",
       " 0.079,\n",
       " 0.08,\n",
       " 0.081,\n",
       " 0.082,\n",
       " 0.083,\n",
       " 0.084,\n",
       " 0.085,\n",
       " 0.086,\n",
       " 0.087,\n",
       " 0.088,\n",
       " 0.089,\n",
       " 0.09,\n",
       " 0.091,\n",
       " 0.092,\n",
       " 0.093,\n",
       " 0.094,\n",
       " 0.095,\n",
       " 0.096,\n",
       " 0.097,\n",
       " 0.098,\n",
       " 0.099]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# l[:][1]\n",
    "x=[]\n",
    "y=[]\n",
    "for i in l:\n",
    "    x.append(i[0])\n",
    "    y.append(i[1])\n",
    "\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'α-W Curve')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EGPenPf4vwC+SH2uBqrSnD0nWeIf1A8DAEEJOsvsi/Xr1MFlZgevHlnL92FJ2HzjB9xfv4kfL9vDk2jpGl/bjk3OH8oFplfTvnZvpUiVJkiRJ71F7ThsJwL8BG2KM30xbH5x22QeAtcn3jwP3hhB6hRCGA6OBJaQadI5OJovkAfcCj8fUP6e/ANyTPP/TwGPt9XnUdVQX5fOV28az6Cs38jf3TKZPXjZ/+tg65vzlc/zJz9ayef/RTJcoSZIkSXoP2nPayHzgFWAN0JYsfxW4j9SRkQjsBH4rae5JCOGPgM8BLaSOmTyZrN8G/B2QDTwQY/x6sj4CeAgoBFYCn0gafr4jp430TG/sOcSDr+/kF6v3caaljdnDC/nk3KHcOrGc3GwbfEqSJElSpp1v2ki7hRedleFFz3bg2Gl+vLyG/1i0i5qDJynp34v7ZlVz36wqBhf0yXR5kiRJktRjGV6kMbwQQGtb5OXNDTz4+k5e3NxAVgjcPL6MT84dyryRRaROPUmSJEmSOsr5wot2bdgpdVbZWYHrx5Vy/bikweeSXTy8dA9PratjRElfPjF7KB+aMYSCPjb4lCRJkqRMc+eFlDjV3MoTa/bx74t2sXL3IXrnZnHXlEo+OXcokyoLMl2eJEmSJHVrHhtJY3ihC7G29jDfX7yLn63cy8nmVqZUDeSTc4Zyx+TB9M7NznR5kiRJktTtGF6kMbzQe3H4ZDOPrqjh3xftYlvDcQr65PLhGUP4+JyhDC/um+nyJEmSJKnbMLxIY3ihixFjZNH2Jv5j0S6eXldHS1vk6tHFfHz2UG4aX0qO41YlSZIk6ZLYsFO6RCEE5o4sYu7IIuqPnOJHS/fwgyW7+e3/WE7ZgF7ce2U1982qprygd6ZLlSRJkqRux50X0kVqaW3j+Y31fH/xbl7ekhq3etP4Uj4+eyjzRxWTleW4VUmSJEm6UO68kNpBTnYWt0ws55aJ5ew+cIIfLNnNw8v28PS6/Qwryudjs6v58IwqBvXNy3SpkiRJktSlufNCuoxOt7Ty1No6vr9oN0t2NpGXk8XtVwzm47OrmTF0ECG4G0OSJEmSzsWGnWkML9RRNtUd5fuLd/HoilqOnm5hbFl/Pj6nmrunVTKgd26my5MkSZKkTsXwIo3hhTraiTMt/HzVXr6/eDeraw7TJzebu6ZW8PHZQ7liSEGmy5MkSZKkTsHwIo3hhTJpdc0hfrB4N4+9sZeTza1MHlLAx2ZV8/6pFeTn2YJGkiRJUs9leJHG8EKdwZFTzfxsZS3/sWgXm/cfo3+vHO6eVsnHZlczfvCATJcnSZIkSR3O8CKN4YU6kxgjy3cd5PuLd/PLNfs409LG9OqBfGz2UO6YPJjeudmZLlGSJEmSOoThRRrDC3VWB4+f4acravjBkt1sbzjOgN45fGjGED4+u5pRpf0zXZ4kSZIktSvDizSGF+rsYows2t7ED5bs5qm1+2hujcwaVsjHZlezYFK5uzEkSZIkdUuGF2kML9SVHDh2mp8sr+GHS3az88AJBuXn8qHpQ7h3VjWjSvtlujxJkiRJumwML9IYXqgramuLLNx2gB8u2c3T6+poaYvMGl7Ix2dXc+tEd2NIkiRJ6voML9IYXqirazj61m6M3U3uxpAkSZLUPRhepDG8UHfhbgxJkiRJ3YnhRRrDC3VHb9+NMTA/lw9OG8J9s6oYXeakEkmSJEmdn+FFGsMLdWdtbZHXtx/gB0t286t1dTS3Rq4cNoh7r6zm9smD3Y0hSZIkqdMyvEhjeKGeovHYaR5ZUcMPl+xhR+NxBvTO4YPTh3DvrCrGlQ/IdHmSJEmS9GsML9IYXqiniTGyaHsTP1yym6fW1nGmtY1p1QO578pq7pgymPy8nEyXKEmSJEmGF+kML9STNR0/k+zG2M22huP065XD+6dWcN+V1VwxpCDT5UmSJEnqwQwv0hheSKndGMt2HeSHS3bzy9X7ON3SxsSKAdw7q5q7plYwoHdupkuUJEmS1MMYXqQxvJB+3eGTzTz2Ri0/XLKHDfuO0Cc3m9snD+a+WVVMrx5ECCHTJUqSJEnqAQwv0hheSOcWY2R1zWEeWrqbx9/Yy/EzrYwp68dHr6zmA9MqKeybl+kSJUmSJHVjhhdpDC+kd3f8dAs/X7WXh5bu4Y09h8jLzuKWiWXce2U180YWkZXlbgxJkiRJl5fhRRrDC+m92Vh3hB8t3cMjK2o5fLKZqsI+fHRmFffMqKK8oHemy5MkSZLUTRhepDG8kC7OqeZWnl5Xx4+W7mHhtgNkBbh+bCkfvbKK68eVkpudlekSJUmSJHVh5wsvcjq6GEldU+/cbO6aWsldUyvZ2Xich5ft4cfLa3huYz0l/Xtxz4whfGRmFcOL+2a6VEmSJEndTLv9U2kIoSqE8EIIYX0IYV0I4fff9vh/CSHEEEJx8nMIIXw7hLA1hLA6hDA97dpPhxC2JF+fTlufEUJYkzzn28GxCFKHGFbcl/++YBwL//AG/uVTM5kypID7X97O9f/rRT76z6/z6MoaTjW3ZrpMSZIkSd1Eux0bCSEMBgbHGFeEEPoDy4G7Y4zrQwhVwL8C44AZMcbGEMJtwO8BtwGzgW/FGGeHEAqBZcBMICavMyPGeDCEsAT4ErAYeAL4dozxyfPV5bERqX3sP3KKnyyv4eFle9h14AT9e+dw99RKPnplFZMqCzJdniRJkqROLiPHRmKM+4B9yfdHQwgbgEpgPfC3wH8HHkt7yl3AgzGVpiwKIQxMApDrgGdijE3Jh3kGWBBCeBEYEGNclKw/CNwNnDe8kNQ+ygb05ovXj+J3rh3J4h1N/Gjpbh5etod/X7SLiRUD+OiVVdw1pZKC/NxMlypJkiSpi+mQnhchhGHANGBxCOEuoDbGuOptpzwqgT1pP9cka+dbrznHuqQMysoKzB1ZxNyRRfz5iWYeW1XLQ0v28KePrePrv9zAgknlfHRmFXNGOHJVkiRJ0oVp9/AihNAP+CnwZaAF+CpwS3u/79tq+ALwBYDq6uqOfGupRyvIz+VTc4fxqbnDWFt7mB8t3cPP3qjlsTf2Ul2Yz4dnDOGemUMYXNAn06VKkiRJ6sTadbZhCCGXVHDx/RjjI8BIYDiwKoSwExgCrAghlAO1QFXa04cka+dbH3KO9d8QY7w/xjgzxjizpKTkcnw0Se/RpMoC/uLuSSz9o5v41r1TGTKoD//7mc1c9Y3n+cx3lvDkmn2caWnLdJmSJEmSOqH2bNgZgO8BTTHGL7/DNTuBmUnDztuB3+Wthp3fjjHOShp2LgfenD6yglTDzqZzNOz8+xjjE+ery4adUuex+8AJfrx8Dz9eVkPdkVMU9s3jA9Mq+cjMKsaW9890eZIkSZI60PkadrZneDEfeAVYA7z5z6lfTQ8X3hZeBOAfgAXACeCzMcZlyXWfI3XcBODrMcbvJOszge8CfUg16vy9+C4fyPBC6nxa2yIvb2ngx8v28Mz6/TS3RqYMKeAjV1Zx55QKBvS2yackSZLU3WUkvOisDC+kzq3p+BkeXVnLw0v3sGn/UXrlZHHbFYP58MwhzBluk09JkiSpuzK8SGN4IXUNMUbW1B7m4WV7eOyNvRw91UJ1YT73zBjCh2YMoXKgTT4lSZKk7sTwIo3hhdT1nDzTytPr6vjR0j28vv0AIcD8UcV8eGYVt0woo3dudqZLlCRJknSJDC/SGF5IXduephP8eHkNP11eQ+2hkwzoncNdU1NNPidVDiDVPkeSJElSV2N4kcbwQuoe2toiC7cd4MfL9/Dk2jrOtLQxrrw/H55Zxd1TKyjq1yvTJUqSJEl6Dwwv0hheSN3P4RPNPL56Lz9ZtodVNYfJzQ7cMK6UD8+o4rqxJeRkZ2W6REmSJEnvwvAijeGF1L1tqjvKj5ft4Wdv1NJ47AzF/XrxwemVfHjGEEaX9c90eZIkSZLegeFFGsMLqWdobm3jhY31/GR5Dc9vrKelLTJlSAH3zKzi/ZMrKMjPzXSJkiRJktIYXqQxvJB6nsZjp/nZylp+sryGjXVHycvJ4taJ5dwzYwjzRxWTnWWTT0mSJCnTDC/SGF5IPVeMkXV7jyTHSvZy+GQz5QN688HplXxoxhBGlvTLdImSJElSj2V4kcbwQhLA6ZZWntuQOlby4qZ62iJMrx7Ih2dWcfvkwQzo7bESSZIkqSMZXqQxvJD0dvVHTvFocqxkS/0xeuVksWBS6ljJvJEeK5EkSZI6guFFGsMLSe8kxsjqmsP8ZHkNj71Ry5FTLQwuSI6VTB/CCI+VSJIkSe3G8CKN4YWkC3GquZVnN+znJ8treHlzw9ljJffMSB0rKejjsRJJkiTpcjK8SGN4Iem92n/k1NlpJVvqjzmtRJIkSWoHhhdpDC8kXawYI2tqD/PT5TU8tmovh040UzagFx+YNoR7ZlQyqrR/pkuUJEmSuizDizSGF5Iuh9MtrbywMTWt5IVNDbS2RaYMKeCD04fw/ikVDOqbl+kSJUmSpC7F8CKN4YWky63x2Gkee2MvP11ew/p9R8jNDtwwrpQPTR/C9eNKyc3OynSJkiRJUqdneJHG8EJSe9qw7wg/XV7Dz97YS+Ox0xT2zeP9Uyq4Z8YQJlYMIAT7Y0iSJEnnYniRxvBCUkdoaW3j5S0N/HR5Lc+s38+Z1jbGlvXng9MruXtaJWUDeme6REmSJKlTMbxIY3ghqaMdPtHMz1fv5ZEVNazYfYisAFeNKuZD04dw68Ry+uRlZ7pESZIkKeMML9IYXkjKpB2Nx3l0RQ0/XVFL7aGT9M3L5rYrBvPB6UOYPbyQLMeuSpIkqYcyvEhjeCGpM2hriyzZ2cQjK2p4Yk0dx063UDmwDx+YVskHplcysqRfpkuUJEmSOpThRRrDC0mdzckzrfxqfR0/XVHLq1saaIswtWogH5xeyZ2THbsqSZKknsHwIo3hhaTOrP7IqdTY1RU1bKw7Sm524PqxpXxweiXXjyulV479MSRJktQ9GV6kMbyQ1FWs33uER1emxq42HD1NQZ9c7pic6o8xvXqgY1clSZLUrRhepDG8kNTVtLS28dq2Azy6ooan1tVxqrmNYUX53D2tkg9Mq2RoUd9MlyhJkiRdMsOLNIYXkrqyY6dbeGptHY+urGHhtgPECDOGDuID0yq5Y/JgBubbH0OSJEldk+FFGsMLSd3FvsMn+dnKvTy6sobN+4/ZH0OSJEldmuFFGsMLSd1NjJF1e4/w6MpaHntjL43HUv0xbp88mA9Oq2TG0EH2x5AkSVKnZ3iRxvBCUnfW0trGq1sbeXRlLb9at5+Tza1UFfbh7qmV3D2tkpEl/TJdoiRJknROhhdpDC8k9RTHT7fw9Lo6Hl1Zy2tbG2mLMGVIAXdPq+TOKRUU9+uV6RIlSZKkswwv0hheSOqJ6o+c4vFVe3l0ZS3r9h4hOytwzehi7p5WyS0TyumTZ38MSZIkZZbhRRrDC0k93eb9R1P9MVbWsvfwKfrmZXPrxHLunlbJvJFF5GRnZbpESZIk9UCGF2kMLyQppa0tsmRnE4+9UcsvVu/j6KkWSvr34v1TKvjAtEomVgyw0ackSZI6TEbCixBCFfAgUAZE4P4Y47dCCH8B3AW0AfXAZ2KMe0PqN+RvAbcBJ5L1FclrfRr44+Sl/2eM8XvJ+gzgu0Af4Ang9+O7fCDDC0n6TaeaW3lxUz2PrqzlhY0NnGltY2RJXz4wrZK7plZSVZif6RIlSZLUzWUqvBgMDI4xrggh9AeWA3cDNTHGI8k1XwImxBh/O4RwG/B7pMKL2cC3YoyzQwiFwDJgJqkQZDkwI8Z4MISwBPgSsJhUePHtGOOT56vL8EKSzu/wiWaeWLuPR1fWsmRHEwAzhg7i7qkV3D65gsK+eRmuUJIkSd3R+cKLnPZ60xjjPmBf8v3REMIGoDLGuD7tsr6kAglI7cZ4MNk5sSiEMDAJQK4DnokxNgGEEJ4BFoQQXgQGxBgXJesPkgpHzhteSJLOryA/l/tmVXPfrGpqDp7g8VV7+dnKWv7ksXX8+c/Xc+2YEu6aVsnN48ts9ClJkqQO0W7hRboQwjBgGqkdEoQQvg58CjgMXJ9cVgnsSXtaTbJ2vvWac6yf6/2/AHwBoLq6+pI+iyT1JEMG5fP/XDeK37l2JBv2HeWxN2p5fNVenttYf7bR513TKrnKRp+SJElqR+0eXoQQ+gE/Bb785nGRGOMfAX8UQvgK8LvAn7VnDTHG+4H7IXVspD3fS5K6oxACEyoGMKFiAH+wYByLd6QafT6xZh+PrKyluF8ed0yu4K6pFUytGmijT0mSJF1W7RpehBBySQUX348xPnKOS75PqlfFnwG1QFXaY0OStVpSR0fS119M1oec43pJUjvKygrMHVnE3JFF/PldE3lhYwOPvVHLD5bs5rsLdzK0KJ+7plTw/qmVjCrtl+lyJUmS1A20W3iRTA/5N2BDjPGbaeujY4xbkh/vAjYm3z8O/G4I4SFSDTsPxxj3hRCeBv4yhDAoue4W4CsxxqYQwpEQwhxSx1E+Bfx9e30eSdJv6pWTzYJJ5SyYVM6RU808tbaOx96o5e9f2Mq3n9/KpMoB3DWlkjunVFBe0DvT5UqSJKmLas9pI/OBV4A1pMaiAnwV+DwwNlnbBfx2jLE2CTv+AVhAalTqZ2OMy5LX+lzyXICvxxi/k6zP5K1RqU8Cv+eoVEnKvP1HTvHzVXt5fNVeVtccJgSYM7yIu6dVsGDiYAryczNdoiRJkjqZjIxK7awMLySpY21rOMZjb+zl8Tdq2XngBHnZWVw3toS7plZy4/hSeuc6sUSSJEmGF7/G8EKSMiPGyOqawzz2xl5+sXov9UdPpyaWTCrnrqlOLJEkSerpDC/SGF5IUua1tkUWbz/AY2/s5Ym1+zh6qoWivnncPnkwd02tYHr1ICeWSJIk9TCGF2kMLySpcznd0sqLmxp4/I29PLthP6db2hgyqA93Tqng/VMqGFfe3yBDkiSpBzC8SGN4IUmd19FTzTyzfj+Pr9rLK1saaW2LjC7tx11TK3j/lEqqi/IzXaIkSZLaieFFGsMLSeoaDhw7zRNr6/j5G3tZsrMJgClVA7lrSgV3TB5M6QBHr0qSJHUnhhdpDC8kqeupPXSSXySjV9ftPUJWgDkjinj/lAoWTCpnYH5epkuUJEnSJTK8SGN4IUld29b6Yzy+ai8/X7WXHY3Hyc0OXDO6hPdPreCm8WX07ZWT6RIlSZJ0EQwv0hheSFL3EGNk3d4jZ4OMfYdP0Ts3ixvHl3Hn5AquG1tC79zsTJcpSZKkC2R4kcbwQpK6n7a2yPLdB3n8jb08sWYfB46foX+vHG6dVM6dUyqYN7KI3OysTJcpSZKk8zC8SGN4IUndW0trGwu3HeDxVXt5em0dR0+3UNg3j/dNKuf9Uyq4clghWVmOXpUkSepsDC/SGF5IUs9xqrmVlzc38PPV+3h2/X5ONrdSNqAXd0yu4M4pFUwZUkAIBhmSJEmdgeFFGsMLSeqZTpxp4dkN9fx81V5e2tTAmdY2qgr7cOfkCu6YXMH4wf0NMiRJkjLI8CKN4YUk6fDJZn61ro7HV+1l4bYDtLZFRpb0TXZkDGZUaf9MlyhJktTjGF6kMbyQJKU7cOw0T66t4xer97J4RxMxwrjy/tw5pYI7J1dQXZSf6RIlSZJ6BMOLNIYXkqR3sv/IKZ5Ys4+fr9rLit2HAJg8pIA7J1dw++TBVAzsk9kCJUmSujHDizSGF5KkC1Fz8AS/XL2PX6zex5rawwDMGDqIOyYP5rYrBlM2oHeGK5QkSepeDC/SGF5Ikt6rHY3H+eXqvfxi9T421h0lBJg1rJA7plTwvknlFPfrlekSJUmSujzDizSGF5KkS7G1/ig/X7WPX6zey7aG42QFmDeymNsnD2bBxHIG9c3LdImSJEldkuFFGsMLSdLlEGNk0/6j/CIJMnYeOEFOVuCqUakg49YJ5RTk52a6TEmSpC7D8CKN4YUk6XKLMbJu7xF+sToVZNQcPEluduDq0SXcfsVgbp5YxoDeBhmSJEnnY3iRxvBCktSeYoysrjnML1bv5Zer97H38CnysrO4ZkwJd04ZzI3jy+jXKyfTZUqSJHU6hhdpDC8kSR2lrS3yRs0hfrFqH0+s2UfdkVPk5WRx/dgSbp9cwY3jSulrkCFJkgRcQngRQvgysBBYEWNsaZ/yOpbhhSQpE9raIst3H+SXq1NBRv3R0/TKyeL6saXcPnkwNxhkSJKkHu5Swov/BcwDxgFrgNdIhRkLY4xN7VBruzO8kCRlWltbZNmug/xy9V6eWFtHw9HT9M799SAjP88gQ5Ik9SyXfGwkhJAHzCQVZMxNvg7FGCdczkI7guGFJKkzaW2LLN3ZxBNr9vHEmjoaj6WCjBvGlXLbFQYZkiSp5zhfeHGhvw31AQYABcnXXlI7MSRJ0iXIzgrMGVHEnBFF/NmdE1myo4lfrtnLU2vreGJN3dkg4/YrKrh+XIlBhiRJ6pHe7djI/cBE4CiwGFgELIoxHuyY8i4/d15IkrqC1rb4a0FG47EzBhmSJKlbu5SdF9VAL2ALUAvUAIcua3WSJOk3ZGcF5o4sYu7IIv78/ZNYsiN1tOTJt+3IeN8km31KkqTu7117XoQQAqndF/OSr0lAE/B6jPHP2r3Cy8ydF5KkruzNHRlvBhlv9si4bkwptyXNPvsZZEiSpC7okht2Ji8yBLiKVIBxB1AUYxx4uYrsKIYXkqTuorUtsmznW0HGm+NXrx1TcnZqSf/euZkuU5Ik6YJcyqjUL/HWjotmkjGpydeaGGPb5S+3fRleSJK6o7a2yPLdB/nl6n08tbaOuiOnyMvJ4prRJdx2RTk3ji+joI9BhiRJ6rwuJbz4JvAasDDGuK+d6utQhheSpO6urS2ycs9Bfrm6jifX7mPf4VPkZgfmjyrmfVcM5pYJZQzMz8t0mZIkSb/mshwb6S4MLyRJPUlbW2RVzaGk0ec+ag6eJCcrMG9UMbdNKueWieUU9jXIkCRJmWd4kcbwQpLUU8UYWVt7hCfW7uOJNfvYdeAE2VmB2cMLed8Vg7l1Yhml/XtnukxJktRDZSS8CCFUAQ8CZUAE7o8xfiuE8DfAncAZYBvw2RjjoeQ5XwE+D7QCX4oxPp2sLwC+BWQD/xpj/EayPhx4CCgClgOfjDGeOV9dhheSJKWCjPX7jvDkmtSOjO2NxwkBrhxayPuuKGfBpHIGF/TJdJmSJKkHyVR4MRgYHGNcEULoTypcuBsYAjwfY2wJIfw1QIzxD0IIE4AfArOACuBZYEzycpuBm4EaYClwX4xxfQjhYeCRGONDIYR/AlbFGP/xfHUZXkiS9OtijGypP5aaWrKmjk37jwIwrXogt00azIJJ5VQV5me4SkmS1N11imMjIYTHgH+IMT6TtvYB4J4Y48eTXRfEGP8qeexp4GvJpV+LMd6arH8lWfsG0ACUJ0HI3PTr3onhhSRJ57et4RhPrU01+1xbewSAKyoLWDCpnPdNKmdESb8MVyhJkrqj84UXOR1UwDBgGrD4bQ99DvhR8n0lsCjtsZpkDWDP29ZnkzoqcijG2HKO69/+/l8AvgBQXV19UZ9BkqSeYmRJP754/Si+eP0odh84wZNr9/HE2jr+5ulN/M3Tmxhb1p8Fk8q57YrBjCnrRwgh0yVLkqRurt3DixBCP+CnwJdjjEfS1v8IaAG+3941xBjvB+6H1M6L9n4/SZK6i+qifH7r2pH81rUj2XvoJE+treOptXV8+/ktfOu5LYwo7pvsyBjMpMoBBhmSJKldtGt4EULIJRVcfD/G+Eja+meAO4Ab41vnVmqBqrSnD0nWeIf1A8DAEEJOsvsi/XpJknSZVQzsw+fmD+dz84dTf/QUv1q3n6fW1vHPL2/n/764jSGD+rBgYqrZ5/TqQWRlGWRIkqTLoz0bdgbge0BTjPHLaesLgG8C18YYG9LWJwI/4K2Gnc8Bo4FAqmHnjaTCiaXAx2KM60IIPwZ+mtawc3WM8f+ery57XkiSdHkdPH6GZzakgoxXtzRyprWN0v69uDUJMmYPLyQnOyvTZUqSpE4uU9NG5gOvAGuAtmT5q8C3gV6kdk4ALIox/nbynD8i1QejhdQxkyeT9duAvyM1KvWBGOPXk/URpEalFgIrgU/EGE+fry7DC0mS2s/RU808v7Gep9bW8eKmBk42tzIwP5ebx5fxvivKuWpUMb1ysjNdpiRJ6oQ6xbSRzsLwQpKkjnHyTCsvbW7g6XV1PLthP0dPtdCvVw43jCtlwaRyrh1TQt9eHdI7XJIkdQEZnzYiSZJ6nj552SyYlDo6cqaljYXbGnlqbR2/Wr+fx1ftpVdOFteMKWHBxHJuGl9GQX5upkuWJEmdlDsvJElSh2ptiyzd2cRTa+t4el0d+w6fIicrMHdkEbdOLOeWiWWU9u+d6TIlSVIH89hIGsMLSZI6jxgjq2sO89S6Op5eW8f2xuOEADOqB3HrxHJunVhOdVF+psuUJEkdwPAijeGFJEmdU4yRrfXHeGptHU+urWP9viMAjB88gAUTy7l1Uhljy/qTGmgmSZK6G8OLNIYXkiR1DXuaTvD0utTRkmW7DhIjDC3KZ8HEcm6ZWM60qoFkZRlkSJLUXRhepDG8kCSp62k4eppn1u/n6XV1LNzWSHNrpLR/L26eUMatE8uZM6KIvJysTJcpSZIugeFFGsMLSZK6tsMnm3lxUz1Pra3jxU0NnGxupX/vHG4cV8qtE8u5dmwJ+XkOVJMkqasxvEhjeCFJUvdxqrmVV7Y08vS6Op7dsJ9DJ5rplZPF1aNLuHViGTeNL2NQ37xMlylJki7A+cIL/1lCkiR1Wb1zs7l5Qhk3TyijpbWNJTub+NW6/WfDjOyswKxhhdw6sYybJ5ZTObBPpkuWJEkXwZ0XkiSp24kxsqb2cNLwcz9b648BcEVlAbdMKOPWSeWMLu3n5BJJkjoRj42kMbyQJKnn2dZw7GzDz5W7DwEwrCifWyaWc+vEMqZVDXJyiSRJGWZ4kcbwQpKknq3+yCl+tX4/v1q/n9eTySXF/VKTS26ZWMa8kUX0ysnOdJmSJPU4hhdpDC8kSdKbjpxq5oWN9fxq3X5e3FTP8TOt9M3L5rqxpdwysYzrxpZS0Cc302VKktQjGF6kMbyQJEnncrqllYXbDvCrdft5Zv1+Go+dJicrMHdkEbdMKOOmCWUMLrDhpyRJ7cXwIo3hhSRJejdtbZGVew7xq/V1PLNuP9sbjwMweUiq4efNE8oZU2bDT0mSLifDizSGF5Ik6b3aWn+Mp9fV8cz6/byx5xAAQ4vyuXl8akzrzGGFZNvwU5KkS2J4kcbwQpIkXYr9R07x7IbU0ZKFWw9wprWNwr553DCulFsmlHH16BL65NnwU5Kk98rwIo3hhSRJulyOnW7hpU0N/Gp9Hc9vrOfoqRZ652Yxf1QJt0wo44bxpRT365XpMiVJ6hLOF17kdHQxkiRJ3UW/XjncPnkwt08eTHNrG4u3N53dlfHshv2EANOrB3HzhNTxkpEl/TJdsiRJXZI7LyRJki6zGCPr9x3hmfWpIGPd3iMAjCjpy80TyrhlQhlTqwbZJ0OSpDQeG0ljeCFJkjpa7aGTPJsEGYu2H6ClLVLcL9Un46bx9smQJAkML36N4YUkScqkwyebeXFTPc9uqOfFjfUcPd1Cr5wsrh5dzE3jy7hxfBkl/e2TIUnqeQwv0hheSJKkzuJMSxtLdzadPV5Se+gkIcDUqoHcND51vGRUaT9C8HiJJKn7M7xIY3ghSZI6oxgjG+uOpo6XbNjP6prDAAwtyuem8WXcNL6MmcMGkZudleFKJUlqH4YXaQwvJElSV1B3+BTPbdzPs+v389q2A5xpaWNA7xyuT/pkXDu2hAG9czNdpiRJl43hRRrDC0mS1NUcP93CK1saeXbDfp7fWE/T8TPkZAXmjCjipvGl3Di+jKrC/EyXKUnSJTG8SGN4IUmSurLWtsjK3Qd5dkM9z6yvY1vDcQDGlvXnpgmpIGPqkIFkOYZVktTFGF6kMbyQJEndyY7G4zy3YT/PbtjP0p0HaW2LFPfrxQ3jSrhpfBnzRxeTn5eT6TIlSXpXhhdpDC8kSVJ3dfhEMy9uTsawbqrn6KkW8nKyuGpkETeOL+PG8aUMLuiT6TIlSTonw4s0hheSJKknaG5tY+mOJp5JdmXsaToJwMSKAdw4voybxpcyqaLA4yWSpE7D8CKN4YUkSeppYoxsrT/GsxvqeW7DflbsPkhbhNL+vbhxfCk3jivjqlHF9MnLznSpkqQezPAijeGFJEnq6ZqOn+GFjfU8t3E/L29u5NjpFnrnZnHVyGJuSMKM8oLemS5TktTDGF6kMbyQJEl6y5mWNhbvOMBzG+p5dsN+ag7++vGSG8eVckWlx0skSe0vI+FFCKEKeBAoAyJwf4zxWyGEDwNfA8YDs2KMy9Ke8xXg80Ar8KUY49PJ+gLgW0A28K8xxm8k68OBh4AiYDnwyRjjmfPVZXghSZJ0bjFGttQf47kN9Ty/cT/Ld6WOl5T078UNY0u5YXwpVzu9RJLUTjIVXgwGBscYV4QQ+pMKF+4mFWS0Af8M/Nc3w4sQwgTgh8AsoAJ4FhiTvNxm4GagBlgK3BdjXB9CeBh4JMb4UAjhn4BVMcZ/PF9dhheSJEkXpun4GV5Kppe8vKmBo6dT00vmjijixvGlXD+2lKrC/EyXKUnqJs4XXrRbbB5j3AfsS74/GkLYAFTGGJ9Jinr7U+4CHooxngZ2hBC2kgoyALbGGLcnz3sIuCt5vRuAjyXXfI/Ujo7zhheSJEm6MIV98/jAtCF8YNqQs9NLnt9Yz/Mb6/nTx9YB6xhb1j/pk1HKtOpBZHu8RJLUDjpkz18IYRgwDVh8nssqgUVpP9ckawB73rY+m9RRkUMxxpZzXC9JkqTLKDc7i3mjipk3qpg/vmMC2xuOnQ0y/uXl7fzji9sYlJ/LdWNLuWFcKdeMKaGgT26my5YkdRPtHl6EEPoBPwW+HGM80t7v9w41fAH4AkB1dXUmSpAkSepWRpT0Y0RJP/7T1SM4cqqZVzY38tzG/by4qYFHV9aSnRWYOXQQN4wr5cbxpYws6XeunbeSJF2Qdg0vQgi5pIKL78cYH3mXy2uBqrSfhyRrvMP6AWBgCCEn2X2Rfv2viTHeD9wPqZ4X7/VzSJIk6Z0N6J3L7ZMHc/vkwbS2Rd7YczDZldHAXz25kb96ciNVhX2Spp9lzB5eSO/c7EyXLUnqQtotvAipaP3fgA0xxm9ewFMeB34QQvgmqYado4ElQABGJ5NFaoF7gY/FGGMI4QXgHlITRz4NPHb5P4kkSZIuVHZWYMbQQmYMLeS/3TqOvYdO8vzGel7YWM+Plu3he6/vok9uNvNHF3PDuFTTz/KC3pkuW5LUybXntJH5wCvAGlLTRQC+CvQC/h4oAQ4Bb8QYb02e80fA54AWUsdMnkzWbwP+jtSo1AdijF9P1keQCi4KgZXAJ5KGn+/IaSOSJEmZcaq5lde3HeC5jft5YWMDtYdOAjBh8IBUkDGulKlVA236KUk9VEZGpXZWhheSJEmZF2Nk0/6jvLCxgRc21rN890Fa2yKFffO4dkwJ148r5drRJRTk2/RTknoKw4s0hheSJEmdz6ETZ3hpcyrIeGlzAwdPNKeOoFQP4vpxpVw/roSxZf1t+ilJ3ZjhRRrDC0mSpM4tvennCxsbWL8vNbCucmAfrhtbwvVjS5k3qoj8vHYfnCdJ6kCGF2kMLyRJkrqWusOneHFTPc9vrOfVrY2cONNKXk4Wc0YUccPY1BGToUV9M12mJOkSGV6kMbyQJEnquk63tLJ0x0Fe2JSaYLK98TgAI0r6cv3Y1PSSK4cPoleOo1glqasxvEhjeCFJktR97Gw8zvMb63lxcwOLth/gTEsb+XnZXDWqmOvHlnLd2BIqBvbJdJmSpAtgeJHG8EKSJKl7OnGmhde3HUh2Zbw1inVceX+uG1vK9WNLmD50ELnZWRmuVJJ0LoYXaQwvJEmSur8YI1vrj50NMpbubKKlLdK/dw5Xjy7murGlXDemhNIBvTNdqiQpYXiRxvBCkiSp5zl6qpnXtjbywsYGXtxcz/4jpwGYWDEg1StjXAlTqwaRneUoVknKFMOLNIYXkiRJPVuMkQ37jvLi5npe3NjA8t0HaW2LFPTJ5erRqV4Z14wpoaR/r0yXKkk9iuFFGsMLSZIkpTt8splXtzTywqZ6XtrcQMPR1K6MKyoLuG5sCdeNdVeGJHUEw4s0hheSJEl6J21tkfX7jvDipnpe3NTAit0HaYswMD+Xq0eXcN2YEq4dW0JxP3dlSNLlZniRxvBCkiRJF+rQiTO8sqWRFzc18NLmBhqP/eaujClDBpLjBBNJumSGF2kMLyRJknQx3mlXRkGfXOaPLj67K6O0vxNMJOliGF6kMbyQJEnS5XD4RDOvbm3kxaRXRn3SK2PC4AHJroxSplUPJNddGZJ0QQwv0hheSJIk6XKLMbUr46XNDby4qYHlu1ITTPr3yuGqUcVcN7aEa8aUUDGwT6ZLlaROy/AijeGFJEmS2tuRU80s3Np4NszYd/gUAGPK+nHtmBKuHVPKlcMH0SsnO8OVSlLnYXiRxvBCkiRJHSnGyJb6Y7y0qYEXN9ezdMdBzrS20Sc3m3kji7h2bAnXjilhaFHfTJcqSRl1vvAip6OLkSRJknqSEAJjyvozpqw///maERw/3cKi7QfOTjB5bmM9AMOK8lO7MsaWMGdEEfl5/qouSW9y54UkSZKUQTsbj/PS5lSQsXBbI6ea28jLzmLW8EKuGVPMtWNKGVPWjxBCpkuVpHblsZE0hheSJEnqrE41t7Js50Fe2pyaYLJ5/zEAygf05poxxVwzpoT5o4oZmJ+X4Uol6fIzvEhjeCFJkqSuYu+hk7y8uYGXtzTw6pZGjpxqISvAlKqBXDsmNcFkypCBZGe5K0NS12d4kcbwQpIkSV1RS2sbq2oO8dLmRl7e3MCqmkPECAV9cpk/qphrx5Rw9ZhiBhc4jlVS12R4kcbwQpIkSd3BweNneHVrKsh4aXMD9UdPA6lxrNeMTu3KmDW8kN65jmOV1DUYXqQxvJAkSVJ3E2Nk0/6jqSMmmxtZsqOJM61t9MrJYvaIIq4ZndqZMarUxp+SOi/DizSGF5IkSeruTp5pZdGOA0mY0cC2huMADC7ozTWjU8dLbPwpqbMxvEhjeCFJkqSepubgCV7Zkjpi8urWRo6eaiEEmDxkINeOLubqMSVMrRpIbnZWpkuV1IMZXqQxvJAkSVJPlmr8eZhXtqR2Zbyx5xBtEfr3ymHuyCKuGVPCNaNLqC7Kz3SpknoYw4s0hheSJEnSWw6fbGbh1kZeTnZm1B46CcDQonyuHl3M1aNLmDuyiAG9czNcqaTuzvAijeGFJEmSdG4xRnY0HueVLY28sqWBhdsOcOJMK9lZgenVA7l6dAlXjy5m8pCBZGfZ+FPS5WV4kcbwQpIkSbowZ1raWLH7IK9saeCVLY2sqT1MjFDQJ5d5I4vOhhlVhR4xkXTpDC/SGF5IkiRJF6fp+Ble2/pW4899h08BMLy4L1ePTk0wmTuyiP4eMZF0EQwv0hheSJIkSZcuxsi2hmPJEZNGFm3/zSMm80cXM7mygBynmEi6AIYXaQwvJEmSpMvvnY6Y9O+d82tHTIYW9c10qZI6KcOLNIYXkiRJUvs7ePwMr21r5NVkZ8abU0yqCvswf1QJ14wuZt7IYgryPWIiKSUj4UUIoQp4ECgDInB/jPFbIYRC4EfAMGAn8JEY48EQQgC+BdwGnAA+E2NckbzWp4E/Tl76f8YYv5eszwC+C/QBngB+P77LBzK8kCRJkjrWm1NMXt2aCjJe33aAY6dbyApwxZCBXD2qmPmji5lePYi8HI+YSD1VpsKLwcDgGOOKEEJ/YDlwN/AZoCnG+I0Qwh8Cg2KMfxBCuA34PVLhxWzgWzHG2UnYsQyYSSoEWQ7MSAKPJcCXgMWkwotvxxifPF9dhheSJElSZjW3trFqzyFe3tLIq1saWFVzmNa2SH5eNrOHFzI/OWIyurQfqX/jlNQTnC+8yGmvN40x7gP2Jd8fDSFsACqBu4Drksu+B7wI/EGy/mCyc2JRCGFgEoBcBzwTY2xKPswzwIIQwovAgBjjomT9QVLhyHnDC0mSJEmZlZudxcxhhcwcVsj/e/MYjpxq5vVtB3hta+qYyQub1gNQ2r8X85NdGfNHFVM6oHeGK5eUKe0WXqQLIQwDppHaIVGWBBsAdaSOlUAq2NiT9rSaZO186zXnWD/X+38B+AJAdXX1JXwSSZIkSZfbgN653DqxnFsnlgNQe+gkryaNP1/YVM8jK2sBGFPWj/mjSpg/uojZw4vo26tD/jojqRNo9/+1hxD6AT8FvhxjPJK+7SvGGEMI7d4xNMZ4P3A/pI6NtPf7SZIkSbp4lQP78NErq/noldW0tUXW7zvCq1sbeW1rI99fvIsHXttBTlZgevUg5o8u5qpRxUwZ4khWqTtr1/AihJBLKrj4fozxkWR5fwhhcIxxX3IspD5ZrwWq0p4+JFmr5a1jJm+uv5isDznH9ZIkSZK6iayswKTKAiZVFvDb147kVHMry3cd5JUtqTDjb5/dzDef2Uz/XjnMGVnE/FGpMGNkSV/7ZUjdSLuFF8n0kH8DNsQYv5n20OPAp4FvJP99LG39d0MID5Fq2Hk4CTieBv4yhDAoue4W4CsxxqYQwpEQwhxSx1E+Bfx9e30eSZIkSZnXOzebq5KAAqDp+Ble33aAV7c28OrWRp5Zvx+AwQW9uWpUqlfGvFFFlPa3X4bUlbXntJH5wCvAGqAtWf4qqaDhYaAa2EVqVGpTEnb8A7CA1KjUz8YYlyWv9bnkuQBfjzF+J1mfyVujUp8Efs9RqZIkSVLPtfvAibNHTF7b1sihE80AjC3rn4QeRcweUUQ/+2VInU5GRqV2VoYXkiRJUs/w9n4ZS3Y0cbqljZyswNSqgcxLdmZMrRpIXo79MqRMM7xIY3ghSZIk9UynmltZsesgr21r5NWtB1hTc4i2CPl52cwaXpg6YjKymHHl/cnKsl+G1NEML9IYXkiSJEkCOHyymUXbD/Da1kZe3drI9objABT1zWPuyKKzPTOqCvMzXKnUM5wvvPCglyRJkqQeqaBPLrdOLOfWieUA7Dt8kte2psKM17Y28ovV+wCoKuzDVSNTTULnjSyiqF+vTJYt9UjuvJAkSZKkt4kxsq3hGK9tPcCrWxtZtP0AR0+1ADCu/K3mn7OG2/xTulw8NpLG8EKSJEnSe9XS2sbavUfO7spYtusgZ5Lmn1OqBnLVyCLmjSpmWvVAeuVkZ7pcqUsyvEhjeCFJkiTpUp1qbmX5roPJSNa3mn/2zs3iymGFzBuZ2pkxsaKAbJt/ShfEnheSJEmSdBn1zs1Ojo4UA6nmn0t2NPHa1kYWbmvkr5/aCMCA3jnMGVHEvKQB6KjSfoRgmCG9V4YXkiRJknSJCvrkcvOEMm6eUAZA/dFTvL7tAAu3HuC1bY38av1+AEr692LeyKLky0km0oXy2IgkSZIktbM9TSdYuK2R17YeYOG2AzQeOw2kJpnMG1HMvFFFzB1ZRGn/3hmuVMoce16kMbyQJEmSlEkxRrbUH2Nh0i8jfZLJ6NJ+zBtZxNyRxcwZUcjA/LwMVyt1HMOLNIYXkiRJkjqT1rbIur2HWbgttStj6Y4mTja3EgJMqihIwowirhxWSF/HsqobM7xIY3ghSZIkqTM709LGqppDZ/tlrNx9kObWeHYs65thxvTqQfTOdSyrug/DizSGF5IkSZK6kpNnWlm2qynVAHTbAVYnY1nzcrKYUT3obJgxpWogudlZmS5XumiGF2kMLyRJkiR1ZUdONbN0x1thxvp9RwDIz8tm5rDCVJgxooiJFQPIMcxQF2J4kcbwQpIkSVJ3cvD4GRbvSAUZr287wJb6YwD075XDrOGFzE12ZowvH0BWVshwtdI7O194YbcXSZIkSerCBvXNY8GkwSyYNBiA+qOnWLQ9tTNj0fYDPLexHoCB+bnMHl7IvJHFzB1ZxOjSfoRgmKGuwZ0XkiRJktSN7Tt8kteTXRkLtx2g9tBJAIr75TF7ROqIyZwRRYws6WuYoYzy2EgawwtJkiRJPdmephOpMGN7KtCoO3IKgNL+vZgzInXEZO6IIoYW5RtmqEN5bESSJEmSBEBVYT5Vhfl85MoqYozsPHCCRUmQ8fr2Azy+ai8Agwt6n92VMXdkEUMG9THMUMa480KSJEmSBECMkW0Nx3l9e6pfxuLtB2g8dgaAyoF9mDOiiDkjCpMwIz/D1aq78dhIGsMLSZIkSbowMUa21h87G2Ys2t5E0/FUmDFkUJ+zOzPmjCyicmCfDFerrs7wIo3hhSRJkiRdnLa2yJb6Y7y+rZHXtx9gyY4mDp5oBqC6MJ85IwqT3RlFVBhm6D0yvEhjeCFJkiRJl0dbW2Rz/dGzY1kX72jikGGGLpLhRRrDC0mSJElqH21tkU37j55tALp4RxOHT/56mDF7uMdMdG6GF2kMLyRJkiSpY7S1RTbUHWHx9iYW7/j1nRlVhX2YM7yI2UkTUBuAyvAijeGFJEmSJGVG+s6MNwONN3tmDBnUh9nDi5g9opC5IxzN2hMZXqQxvJAkSZKkzuHNnhmLkiMmi3e8Nc2koqA3s0cUMXt4qm/G0KJ8w4xuzvAijeGFJEmSJHVOMaammSxOxrIu3nGAxmOpMKNsQK+zOzPmjChiRHFfw4xuxvAijeGFJEmSJHUNMUa2NRxj0fams9NMGo6eBqC4X69UkDG8kNkjihhd2s8wo4s7X3iR09HFSJIkSZJ0IUIIjCrtz6jS/nxizlBijOxoPJ46YpKEGb9cvQ+Awr55zBpWyOxkosm48v5kZRlmdBeGF5IkSZKkLiGEwIiSfowo6cd9s6qJMbKn6SSLdqQagC7afoCn1tUBMKB3DrOGF549ajJh8ABysrMy/Al0sQwvJEmSJEldUgiB6qJ8qovy+cjMKgBqDp5gyY4mFm9vYsnOJp7dUA9Av145zBg6KNmZUcgVlQPJyzHM6CrseSFJkiRJ6rb2Hzl19pjJkh1NbKk/BkDv3CymVw9i9vAiZg0vZFr1QHrnZme42p7Nhp1pDC8kSZIkqec6cOw0S3akdmUs3t7EhrojxAi52YEpQwYye0Qhs4YXMWPoIPr18rBCR8pIeBFCeAC4A6iPMU5K1qYA/wT0A3YCH48xHkke+wrweaAV+FKM8elkfQHwLSAb+NcY4zeS9eHAQ0ARsBz4ZIzxzLvVZXghSZIkSXrT4ZPNLN/VlOzOaGJN7WFa2yLZWYGJFQOYNayQWcMLuXJYIYP65mW63G4tU+HFNcAx4MG08GIp8F9jjC+FED4HDI8x/kkIYQLwQ2AWUAE8C4xJXmozcDNQAywF7osxrg8hPAw8EmN8KITwT8CqGOM/vltdhheSJEmSpHdy/HQLK3cfYvGO1DGTlXsOcaalDYBx5f2ZNTwVZswaVkjpgN4ZrrZ7ydixkRDCMOAXaeHFYWBgjDGGEKqAp2OME5JdF8QY/yq57mnga8nLfC3GeGuy/pVk7RtAA1AeY2wJIcxNv+58DC8kSZIkSRfqdEsrq2sOs2RHaprJ8l0HOXGmFYDhxX25ctggZg0vYvbwQoYM6kMIjme9WOcLLzr6AM864C7gZ8CHgapkvRJYlHZdTbIGsOdt67NJHRU5FGNsOcf1vyGE8AXgCwDV1dWX9AEkSZIkST1Hr5xsrhyWOjbyxetH0dLaxrq9R1ITTXY08fS6/Ty8rAaA8gG9U0dMkp0Zo0v7kZVlmHE5dHR48Tng2yGEPwEeB961R8XlEGO8H7gfUjsvOuI9JUmSJEndT052FlOqBjKlaiD/+ZoRtLVFttQfY8mOAyzZeZDFOw7w+Kq9AAzMz2Xm0NRo1iuHFzKxYgC52Y5nvRgdGl7EGDcCtwCEEMYAtycP1fLWLgyAIcka77B+ABgYQshJdl+kXy9JkiRJUofIygqMLe/P2PL+fHLuMGKM7G46kZposqOJpTubeHbDfgDy87KZXj0otZNj+CCmVQ2iT57jWS9Eh4YXIYTSGGN9CCEL+GNSk0cgtQvjByGEb5Jq2DkaWAIEYHQyWaQWuBf4WNIz4wXgHlITRz4NPNaRn0WSJEmSpLcLITC0qC9Di/ry4Zmpf4vff+QUS3c2nQ00/u65zWfHs15RWXD2mMnMoYUU5Odm+BN0Tu05beSHwHVAMbAf+DNSI1K/mFzyCPCVmBQQQvgjUsdKWoAvxxifTNZvA/6O1KjUB2KMX0/WR5AKLgqBlcAnYoyn360uG3ZKkiRJkjLp8Ilmlu9O9cxYuiM1nrW5NRICjC3rn+zMSAUa5QU9Z6JJxqaNdEaGF5IkSZKkzuTkmVZW7jnIsp0HWbqz6dcmmlQV9uHKYakg48rhhYwo7tttJ5p0pmkjkiRJkiQpTZ+8bOaNLGbeyGIAWlrbWL/vyNmeGS9tauCRFak2j0V985g5bNDZCSgTKwaQ0wOagLrzQpIkSZKkTizGyPbG4yzd0cSSnU0s23mQ3U0ngLeagM4cNohZwwqZWj2Q/LyuuU/BYyNpDC8kSZIkSV1d3eFUE9BlO5tYsvMgG+uOECPkZAUmVhbwjQ9ewfjBAzJd5nvisRFJkiRJkrqR8oLe3DmlgjunVABw+GQzK3YfZOmO1M6Mon55Ga7w8jK8kCRJkiSpiyvok8v1Y0u5fmxppktpF92/q4ckSZIkSerSDC8kSZIkSVKnZnghSZIkSZI6NcMLSZIkSZLUqRleSJIkSZKkTs3wQpIkSZIkdWqGF5IkSZIkqVMzvJAkSZIkSZ2a4YUkSZIkSerUDC8kSZIkSVKnZnghSZIkSZI6NcMLSZIkSZLUqRleSJIkSZKkTi3EGDNdQ4cKITQAuzJdR5pioDHTRUjtzPtcPYX3unoC73P1BN7n6ik6270+NMZYcq4Helx40dmEEJbFGGdmug6pPXmfq6fwXldP4H2unsD7XD1FV7rXPTYiSZIkSZI6NcMLSZIkSZLUqRleZN79mS5A6gDe5+opvNfVE3ifqyfwPldP0WXudXteSJIkSZKkTs2dF5IkSZIkqVMzvGhHIYQFIYRNIYStIYQ/PMfjvUIIP0oeXxxCGJb22FeS9U0hhFs7tHDpPbjY+zyEcHMIYXkIYU3y3xs6vHjpAl3Kn+fJ49UhhGMhhP/aYUVLF+ESf3eZHEJ4PYSwLvmzvXeHFi9doEv43SU3hPC95P7eEEL4SocXL12gC7jPrwkhrAghtIQQ7nnbY58OIWxJvj7dcVWfn+FFOwkhZAP/B3gfMAG4L4Qw4W2XfR44GGMcBfwt8NfJcycA9wITgQXA/01eT+pULuU+JzVP+s4Y4xXAp4F/75iqpffmEu/zN30TeLK9a5UuxSX+7pID/Afw2zHGicB1QHMHlS5dsEv8M/3DQK/kd5cZwG+9PayWOoMLvM93A58BfvC25xYCfwbMBmYBfxZCGNTeNV8Iw4v2MwvYGmPcHmM8AzwE3PW2a+4Cvpd8/xPgxhBCSNYfijGejjHuALYmryd1Nhd9n8cYV8YY9ybr64A+IYReHVK19N5cyp/nhBDuBnaQus+lzuxS7vVbgNUxxlUAMcYDMcbWDqpbei8u5T6PQN8krOsDnAGOdEzZ0nvyrvd5jHFnjHE10Pa2594KPBNjbIoxHgSeIfUP6hlneNF+KoE9aT/XJGvnvCbG2AIcBoou8LlSZ3Ap93m6DwErYoyn26lO6VJc9H0eQugH/AHw5x1Qp3SpLuXP9DFADCE8nWxD/u8dUK90MS7lPv8JcBzYR+pfrf9XjLGpvQuWLsKl/H2y0/5dNCfTBUjq2UIIE0ltx7wl07VI7eBrwN/GGI8lGzGk7ioHmA9cCZwAngshLI8xPpfZsqTLahbQClQAg4BXQgjPxhi3Z7YsqWdw50X7qQWq0n4ekqyd85pk+1kBcOACnyt1BpdynxNCGAI8Cnwqxrit3auVLs6l3Oezgf8vhLAT+DLw1RDC77ZzvdLFupR7vQZ4OcbYGGM8ATwBTG/3iqX37lLu848BT8UYm2OM9cBrwMx2r1h67y7l75Od9u+ihhftZykwOoQwPISQR6oB5+Nvu+ZxUo0KAe4Bno8xxmT93qTT8XBgNLCkg+qW3ouLvs9DCAOBXwJ/GGN8raMKli7CRd/nMcarY4zDYozDgL8D/jLG+A8dVLf0Xl3K7y5PA1eEEPKTv+xdC6zvoLql9+JS7vPdwA0AIYS+wBxgY4dULb03F3Kfv5OngVtCCIOSRp23JGsZ57GRdhJjbEn+de1pIBt4IMa4LoTwP4BlMcbHgX8D/j2EsBVoInVTkVz3MKn/p98CfNGmV+qMLuU+B34XGAX8aQjhT5O1W5J/yZA6jUu8z6Uu4xJ/dzkYQvgmqV+YI/BEjPGXGfkg0nlc4p/p/wf4TghhHRCA7yQND6VO5ULu8xDClaR2QA8C7gwh/HmMcWKMsSmE8Bek/jwH+B+dpbdLSIWIkiRJkiRJnZPHRiRJkiRJUqdmeCFJkiRJkjo1wwtJkiRJktSpGV5IkiRJkqROzfBCkiRJkiR1aoYXkiSpWwohfC2E8F/PsT4shLA2EzVJkqSLY3ghSZIkSZI6NcMLSZKUcSGEPw4hrAkhrAwhzA8h/Pwc13w+hLA5hLAkhPAvIYR/SNaHhRCeDyGsDiE8F0KoPsdzZ4QQVoUQVgFf7ICPJEmSLiPDC0mSlFEhhDnAh4BpwF8DPwF+8bZrKoA/AeYAVwHj0h7+e+B7McbJwPeBb5/jbb4D/F6Mccpl/wCSJKndGV5IkqRMmwv8MsbYAjwFlPK28AKYBbwUY2yKMTYDP37b83+QfP/vwPz0J4YQBgIDY4wvp10jSZK6kJxMFyBJkgScTvtvLVAXQngjWXscWJGJoiRJUufgzgtJkpRpy0gdBQF4P1ABFMYYpyZffwosBa4NIQwKIeSQOmbypoXAvcn3HwdeSX/xGOMh4FAIYX7aNZIkqQtx54UkScqoGOMrIYR1IYQngL7A54FHQgi3xhhPJNfUhhD+ElgCNAEbgcPJS/we8J0Qwn8DGoDPnuNtPgs8EEKIwK/a9xNJkqTLLcQYM12DJEnSuwoh9IsxHkt2XjwKPBBjfDTTdUmSpPbnsRFJktRVfC3pg7EW2AH8LKPVSJKkDuPOC0mSJEmS1Km580KSJEmSJHVqhheSJEmSJKlTM7yQJEmSJEmdmuGFJEmSJEnq1AwvJEmSJElSp2Z4IUmSJEmSOrX/H3SyIPkMJ3G3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(18,6))\n",
    "plt.plot(x,y)\n",
    "plt.xlabel('α-gold')\n",
    "plt.ylabel('W')\n",
    "plt.title('α-W Curve')"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "b5fc3e43e814ad01a3162358639356c8d5269fa9e7a0a06af2b9cdf0ba130cad"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
